Speech Disorders Classification by CNN in Phonetic E-Learning System

Liu, Jueting; Ren, Chang; Luan, Yaoxuan; Li, Sicheng; Xie, Tianshi; Seals, Cheryl; Atkins, Marisha Speights

doi:10.1007/978-3-031-05643-7_36

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…In the phonetic transcription exam, teachers need to upload the pronunciation of English words, phrases, and sentences, where the pronunciation of speech sounds can be represented as phonemes. G2P conversion is a process that converts written words (grapheme) to their pronunciations (phonemes) [6]. With this G2P converter, teachers can easily extract the words in their phonetic format.…”

Section: Introductionmentioning

confidence: 99%

AI Enhancements for Linguistic E-Learning System

Liu,

Li,

Ren

et al. 2023

Applied Sciences

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E-learning systems have been considerably developed after the COVID-19 pandemic. In our previous work, we developed a linguistic interactive E-learning system for phonetic transcription learning. In this paper, we propose three artificial-intelligence-based enhancements to this system from different aspects. Compared with the original system, the first enhancement is a disordered speech classification module; this module is driven by the MFCC-CNN model, which aims to distinguish disordered speech and nondisordered speech. The accuracy of the classification is about 83%. The second enhancement is a grapheme-to-phoneme converter. This converter is based on the transformer model and designed for teachers to better generate IPA words from the regular written text. Compared with other G2P models, our transformer-based G2P model provides outstanding PER and WER performance. The last part of this paper focuses on a Tacotron2-based IPA-to-speech synthesis system, this deep learning-based TTS system can help teacher generate high-quality speech sounds from IPA characters which significantly improve the functionality of our original system. All of these three enhancements are related to the phonetic transcription process. and this work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field.

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Section: Introductionmentioning

confidence: 99%

AI Enhancements for Linguistic E-Learning System

Liu,

Li,

Ren

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

AI Enhancements for Linguistic E-learning System

Liu,

Li,

Ren

et al. 2023

Preprint

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The E-learning system has achieved great development after the pandemic. In this work, we proposed three artificial intelligence-based enhancements to our linguistic interactive E-learning system from different aspects. Compared with the original phonetic transcription exam system, our enhancements include an MFCC+CNN-based disordered speech classification module, a Transformer-based Grapheme-to-Phoneme converter, and a Tacotron2-based IPA-to-Speech speech synthesis system. This work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field.

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Speech Disorders Classification by CNN in Phonetic E-Learning System

Cited by 2 publications

References 7 publications

AI Enhancements for Linguistic E-Learning System

AI Enhancements for Linguistic E-Learning System

AI Enhancements for Linguistic E-learning System

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